@INPROCEEDINGS{ChenAlghamdiEtAl19a,
AUTHOR = {Chen, J. and Alghamdi, G. and Schmidt, R. A. and Walther, D. and Gao, Y.},
YEAR = {2019},
TITLE = {Ontology Extraction for Large Ontologies via Modularity and Forgetting},
EDITOR = {Kejriwal, M. and Szekely, P. A. and Troncy, R.},
BOOKTITLE = {Proceedings of the 10th International Conference on Knowledge Capture (K-CAP’19)},
PUBLISHER = {ACM},
PAGES = {45--52},
DOI = {https://doi.org/10.1145/3360901.3364424},
ABSTRACT = {We are interested in the computation of ontology extracts based
on forgetting from large ontologies in real-world scenarios. Such
scenarios require nearly all of the terms in the ontology to be forgot-
ten, which poses a significant challenge to forgetting tools. In this
paper we show that modularization and forgetting can be combined
beneficially in order to compute ontology extracts. While a module
is a subset of axioms of a given ontology, the solution of forgetting
(also known as a uniform interpolant) is a compact representation
of the ontology limited to a subset of the signature. The approach
introduced in this paper uses an iterative workflow of four stages:
(i) extension of the given signature and, if needed partitioning,
(ii) modularization, (iii) forgetting, and (iv) evaluation by domain
expert. For modularization we use three kinds of modules: locality-
based, semantic and minimal subsumption modules. For forgetting
three tools are used: Nui, Lethe and Fame. An evaluation on the
SNOMED CT and NCIt ontologies for standard concept name lists
showed that precomputing ontology modules reduces the number
of terms that need to be forgotten. An advantage of the presented
approach is high precision of the computed ontology extracts.
}
}